The connectivity estimation routines in MNE-Python are designed to be flexible yet computationally efficient. When estimating connectivity in sensor-space, an instance of Epochs is used as input to the connectivity estimation routine. For source-space connectivity estimation, a Python list containing SourceEstimate instances is used. Instead of a list, it is also possible to use a Python generator object which produces SourceEstimate instances. This option drastically reduces the memory requirements, as the data is read on-demand from the raw file and projected to source-space during the connectivity computation, therefore requiring only a single SourceEstimate instance to be kept in memory. To use this feature, inverse methods which operate on Epochs, e.g., apply_inverse_epochs, have the option to return a generator object instead of a list. For linear inverse methods, e.g., MNE, dSPM, sLORETA, further computational savings are achieved by storing the inverse kernel and sensor-space data in the SourceEstimate objects, which allows the connectivity estimation routine to exploit the linearity of the operations and apply the time-frequency transforms before projecting the data to source-space.
